research-article

TriMLP: A Foundational MLP-Like Architecture for Sequential Recommendation

Authors:

Hui XiongAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 6

Article No.: 157, Pages 1 - 34

https://doi.org/10.1145/3670995

Published: 18 October 2024 Publication History

Abstract

In this work, we present TriMLP as a foundational MLP-like architecture for the sequential recommendation, simultaneously achieving computational efficiency and promising performance. First, we empirically study the incompatibility between existing purely MLP-based models and sequential recommendation, that the inherent fully-connective structure endows historical user–item interactions (referred as tokens) with unrestricted communications and overlooks the essential chronological order in sequences. Then, we propose the MLP-based Triangular Mixer to establish ordered contact among tokens and excavate the primary sequential modeling capability under the standard auto-regressive training fashion. It contains (1) a global mixing layer that drops the lower-triangle neurons in MLP to block the anti-chronological connections from future tokens and (2) a local mixing layer that further disables specific upper-triangle neurons to split the sequence as multiple independent sessions. The mixer serially alternates these two layers to support fine-grained preferences modeling, where the global one focuses on the long-range dependency in the whole sequence, and the local one calls for the short-term patterns in sessions. Experimental results on 12 datasets of different scales from 4 benchmarks elucidate that TriMLP consistently attains favorable accuracy/efficiency tradeoff over all validated datasets, where the average performance boost against several state-of-the-art baselines achieves up to 14.88%, and the maximum reduction of inference time reaches 23.73%. The intriguing properties render TriMLP a strong contender to the well-established RNN-, CNN-, and Transformer-based sequential recommenders. Code is available at https://github.com/jiangyiheng1/TriMLP.

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Cited By

Yang XDong XYu JLi SXiong XShen H(2025)Locally enhanced denoising self-attention networks and decoupled position encoding for sequential recommendationComputers and Electrical Engineering10.1016/j.compeleceng.2025.110064123(110064)Online publication date: Apr-2025
https://doi.org/10.1016/j.compeleceng.2025.110064
Liu ZLiu SZhang ZCai QZhao XZhao KHu LJiang PGai KHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term RetentionProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657829(1872-1882)Online publication date: 11-Jul-2024
https://doi.org/10.1145/3626772.3657829
Chen JWang HShang J(2024)GeoMamba: Toward Efficient Geography-Aware Sequential POI RecommendationIEEE Access10.1109/ACCESS.2024.348211612(167906-167918)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3482116

Index Terms

TriMLP: A Foundational MLP-Like Architecture for Sequential Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 6

November 2024

813 pages

EISSN:1558-2868

DOI:10.1145/3618085

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 18 October 2024

Online AM: 10 June 2024

Accepted: 24 May 2024

Revised: 31 March 2024

Received: 12 November 2023

Published in TOIS Volume 42, Issue 6

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National Natural Science Foundation of China
Natural Science Foundation of China for Young Scholars
Jilin Education Science Foundation
Jilin Science and Technology Research Project
Jilin Science and Technology Development Project
Science and Technology Development Fund (FDCT), Macau SAR
Start-up Research Grant of University of Macau

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Yang XDong XYu JLi SXiong XShen H(2025)Locally enhanced denoising self-attention networks and decoupled position encoding for sequential recommendationComputers and Electrical Engineering10.1016/j.compeleceng.2025.110064123(110064)Online publication date: Apr-2025
https://doi.org/10.1016/j.compeleceng.2025.110064
Liu ZLiu SZhang ZCai QZhao XZhao KHu LJiang PGai KHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term RetentionProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657829(1872-1882)Online publication date: 11-Jul-2024
https://doi.org/10.1145/3626772.3657829
Chen JWang HShang J(2024)GeoMamba: Toward Efficient Geography-Aware Sequential POI RecommendationIEEE Access10.1109/ACCESS.2024.348211612(167906-167918)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3482116

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